The learning efficiency can also be significantly improved by making better use of knowledge. [1]} introduces a metaphor to interpret how humans use knowledge for problem solving. It says that your brain has a System 1 that uses implicit knowledge for quick, instinctive responses, and a System 2 that uses explicit knowledge for slower, more thoughtful processing. We can find many AI systems that work similarly to Systems 1 and 2, respectively. For example, a close-book QA system based on GPT-3 [2]} is like System 1. It assumes that all the knowledge has been encoded in the model parameters, and the system can rely solely on the model to generate answers without accessing any external knowledge, e.g., by retrieving relevant Web documents. An open-book QA system based on a retriever-reader pipeline (Chapter ) works like System 2. Given an input query, the system first retrieves relevant documents, entities, and other forms of explicit knowledge from document collections or knowledge bases, and then generates the answer from the retrieved knowledge. There have been recent works on integrating explicit and implicit knowledge for open-domain question answering [3]}, [4]}.
